Novel View Synthesis (NVS) is the problem of synthesising unseen camera views from a set of known viewsThroughout the paper, we will use the term viewpoint to refer to the camera pose, view to refer to the scene seen through a certain viewpoint and to image to refer to the photometric content captured from a view. [1]}, [2]}.
NVS is an enabling technology that can be used for augmented or virtual reality [3]}, entertainment [4]}, and robotics [5]} applications.
NVS has undergone a significant improvement after the introduction of Neural Radiance Fields (NeRF) [6]}, [7]}, which consists of a trainable implicit neural representation of a 3D scene that can photorealistically render unseen (novel) views.
NeRF is typically trained with several images, e.g. about hundred images taken from different and uniformly distributed camera viewpoints around an object of interest [6]}.
If viewpoints are not uniformly distributed or they are few (e.g. four or eight), the resulting NeRF model may fail to produce satisfactory renderings [9]}, [10]}.
This is a practical problem that may occur when non-experts acquire images of an object in the wild [11]} or when it is not possible to view objects from every side.
When NeRF is trained on few and non-uniformly distributed viewpoints, the likelihood of overfitting on these viewpoints increases, namely the few-shot view synthesis problem [9]}.
Approaches that aim at addressing few-shot view synthesis include DietNeRF and pixelNeRF [9]}, [14]}.
The drawback of these methods is that they rely on auxiliary deep networks, which have to be trained on other datasets, thus requiring additional loss terms and increasing NeRF's computational burden.
Alternatively, data augmentation solutions can be used to mitigate overfitting [15]}, however they have not yet been explored for few-shot view synthesis.
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